Calibrating the building energy model with the short term monitored data

2020 ◽  
Vol 224 ◽  
pp. 110207
Author(s):  
Faith Tüysüz ◽  
Hatice Sözer
2016 ◽  
Vol 172 ◽  
pp. 251-263 ◽  
Author(s):  
Can Cui ◽  
Teresa Wu ◽  
Mengqi Hu ◽  
Jeffery D. Weir ◽  
Xiwang Li

2021 ◽  
Vol 252 ◽  
pp. 111380
Author(s):  
José Eduardo Pachano ◽  
Carlos Fernández Bandera

2021 ◽  
Vol 19 (1) ◽  
pp. 121-139
Author(s):  
Maciej Mróz

Access to energy resources has become one of the main challenges of energy security in the modern world. Due to the growing political instability of countries exporting energy resources, the energy security category should be perceived in a special way. Energy security is expressed, first of all, in stable access to energy resources at an acceptable price. The aim of the article is to determine to what extent the strategy of diversifying the sources of crude oil imports to Poland and Belarus is implemented in practice, and to what extent the implementation of this strategy is conducive to building energy security for both these countries. It seems that despite the similar geological and geographical conditions, as well as the common historical infrastructure heritage, Poland and Belarus shape their energy policy differently. The conducted analysis has an empirical dimension, as the REES index is used to measure the concentration of imports and the short-term risk for the security of crude oil supplies. The article shows that a properly implemented diversification strategy significantly improves the country’s energy security, which is possible due to the use of alternative directions for oil imports to the Russian one.


2019 ◽  
Vol 202 ◽  
pp. 109377 ◽  
Author(s):  
Yu-Chen Wang ◽  
Zheng-Fu Bian ◽  
Kai Qin ◽  
Yu Zhang ◽  
Shao-Gang Lei

Author(s):  
Luxi Jin ◽  
Sebastian Schubert ◽  
Daniel Fenner ◽  
Fred Meier ◽  
Christoph Schneider

Abstract We report the ability of an urban canopy model, coupled with a regional climate model, to simulate energy fluxes, the intra-urban variability of air temperature, urban-heat-island characteristics, indoor temperature variation, as well as anthropogenic heat emissions, in Berlin, Germany. A building energy model is implemented into the Double Canyon Effect Parametrization, which is coupled with the mesoscale climate model COSMO-CLM (COnsortium for Small-scale MOdelling in CLimate Mode) and takes into account heat generation within buildings and calculates the heat transfer between buildings and the urban atmosphere. The enhanced coupled urban model is applied in two simulations of 24-day duration for a winter and a summer period in 2018 in Berlin, using downscaled reanalysis data to a final grid spacing of 1 km. Model results are evaluated with observations of radiative and turbulent energy fluxes, 2-m air temperature, and indoor air temperature. The evaluation indicates that the improved model reproduces the diurnal characteristics of the observed turbulent heat fluxes, and considerably improves the simulated 2-m air temperature and urban heat island in winter, compared with the simulation without the building energy model. Our set-up also estimates the spatio–temporal variation of wintertime energy consumption due to heating with canyon geometry. The potential to save energy due to the urban heat island only becomes evident when comparing a suburban site with an urban site after applying the same grid-cell values for building and street widths. In summer, the model realistically reproduces the indoor air temperature and its temporal variation.


2018 ◽  
Vol 11 (1) ◽  
pp. 147 ◽  
Author(s):  
Byung-Ki Jeon ◽  
Eui-Jong Kim ◽  
Younggy Shin ◽  
Kyoung-Ho Lee

The aim of this study is to develop a model that can accurately calculate building loads and demand for predictive control. Thus, the building energy model needs to be combined with weather prediction models operated by a model predictive controller to forecast indoor temperatures for specified rates of supplied energy. In this study, a resistance–capacitance (RC) building model is proposed where the parameters of the models are determined by learning. Particle swarm optimization is used as a learning scheme to search for the optimal parameters. Weather prediction models are proposed that use a limited amount of forecasting information fed by local meteorological centers. Assuming that weather forecasting was perfect, hourly outdoor temperatures were accurately predicted; meanwhile, differences were observed in the predicted solar irradiances values. In investigations to verify the proposed method, a seven-resistance, five-capacitance (7R5C) model was tested against a reference model in EnergyPlus using the predicted weather data. The root-mean-square errors of the 7R5C model in the prediction of indoor temperatures on all the specified days were within 0.5 °C when learning was performed using reference data obtained from the previous five days and weather prediction was included. This level of deviation in predictive control is acceptable considering the magnitudes of the loads and demand of the tested building.


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